FIT5217 Natural language processing
Faculty of Information Technology
FIT5217 Natural language processing is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology, offered in 2022 in Semester 1 at Clayton and Suzhou (SEU). It needs FIT5047.
- Credit points
- 6
- Offered in 2022
- Semester 1
- Clayton, Suzhou (SEU)
- Assessment
- Exam 120%
- and 4 other tasks
- Workload
- 144 hours
- per semester
This is the 2022 handbook entry. See the 2027 entry.
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Requisites
Before FIT5217
Prohibitions
You can't enrol if you have passed any of these.
Prerequisites
Pass these before you enrol.
After FIT5217
No unit lists FIT5217 as a prerequisite in the 2022 handbook.
Enrolment rules
Prerequisite: For students enrolled in E3001, E3002, E3005, E3010, E3011, E3007 completing the Software Engineering specialisation: ENG1005 AND FIT3080.
Prerequisite: Basics of probability and mathematics (discrete & continuous)
Equivalent units
The same content under another code. Only one of them counts.
Overview
Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. This unit introduces fundamentals of NLP. It covers techniques for the analysis of words, sentences, and documents as well as applications including information extraction, and question answering.
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| Term 3 | Suzhou (SEU) | On campus |
Assessment
- Assignment 1AssignmentThreshold hurdle20%
- Assignment 2AssignmentThreshold hurdle20%
- Examination (2 hours and 10 minutes)ExamThreshold hurdle60%
- Assignment 1AssignmentThreshold hurdle20%
- QuizzesOtherThreshold hurdle20%
- Scheduled final assessment (2 hours and 10 minutes)ExamThreshold hurdle60%
Learning outcomes
When you finish this unit, you should be able to:
- 1
organise core problems and applications in NLP;
- 2
design systems to tackle NLP problems;
- 3
evaluate NLP systems;
- 4
assess various approaches to NLP.
Workload and teaching
- Lectures24 hours
- Laboratories24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.
Learning resources
Recommended resources
- Speech and Language Processing (3rd ed. draft), Dan Jurafsky and James H. Martin, Draft chapters in progress, October 16, 2019. The PDF can be obtained here: https://web.stanford.edu/~jurafsky/slp3/
- Foundations of Statistical Natural Language Processing, Chris Manning and Hinrich Schütze, MIT Press. Cambridge, MA: May 1999. The book's website: https://nlp.stanford.edu/fsnlp/
-
Introduction to Natural Language Processing, Jacob Eisenstein, MIT Press. Cambridge, 2019.
Where it fits
FIT5217 is part of 2 areas of study in the 2022 handbook.
Contacts
- Chief Examiners
- Dr Ehsan Shareghi Nojehdeh
Common questions
What are the prerequisites for FIT5217?
You need FIT5047 before you enrol. Enrolment rules also apply.
When is FIT5217 offered?
In 2022, FIT5217 runs in Semester 1 at Clayton and Suzhou (SEU).
How much work is FIT5217?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT5217 have an exam?
Yes. The exam is worth 120% of the final mark, alongside 5 other tasks.
Which majors and minors include FIT5217?
FIT5217 is part of Computational science and Software engineering.